Eyes Health Care Blog

Machine vision preserves facial treatment

While data protection concerns have been a factor for many years, it turns out that if you put a useful application alongside the machine vision algorithm – that is, entertain – everyone is happy. For example, a Russian music festival uses a facial recognition algorithm for participants with photos of events, while a Singapore company develops a traffic ticket system that develops volunteer face recognition to commuter prizes [19659002] Helps consumers interact with face detection technology in the hands of their hands. Mobile apps, such as FaceLock, scans the face of the user to unlock applications on your smartphone or tablet. In addition, according to Apple's latest announcement, the new generation of iPhone will use "profound information to enhance face recognition." Users rely on Face Detection of Critical Tasks such as Mobile Banking and Commerce

The foreseeable increase in face recognition and other biometric usage reflects these trends. The Face Detection Market will rise from $ 3.3 billion in 2016 to $ 6.84 billion in 2021. Analysts attribute growth to the growing supervisory market, increasing government deployment and other applications in identity management.

finds the way to facelift growth, regardless of whether it is a blue-light calibrated camera or a mobile application that helps police suspects. But the technique must first get rid of some hiccups.

Redact and Serve

Suspect Technologies, a Massachusetts-based company in Cambridge, developed advanced face detection algorithms but has two different purposes in the law enforcement. One use deals with considerations of private life around body cameras worn by policemen. The most commonly referred to bodyworm video is to improve law enforcement responsibility and transparency. If a Patent Lawyer purchases such a video, law enforcement agencies must respond promptly.

But they can not do this without first blurring the identity of victims, minors and innocent viewers, which is typically a slow, tedious process limited to video professionals. Suspect Technologies' automated video redaction (AVR) software, available on VIEVU-equipped cameras, is adapted to the real-world conditions of BWV – most of all with high motion and low illumination. Simultaneous tracking technology across multiple objects provides a simple interface that allows users to add or adjust new objects. AVR reduces the time to modify video footage tenfold for existing methods.

Unlike the AVR, which covers identity, Suspect Technologies launches a mobile information application to identify suspects. "As it stands now, there is no easy way for law enforcement agencies to say that someone is a criminal," says Jacob Sniff, CEO and Technical Manager at Suspect Technologies.

Compatible with iPhone and Android devices, with the company's cloud-based checklist, the recognition software was tested for 10 million pages. The algorithm uses the right face recognition accuracy, which increases tenfold every four years. "Our goal is to have 100% accuracy in the order of 10,000 identities," says Sniff

Suspect Technologies customizes products in regional law enforcement agencies in mid-sized cities, typically around 100 people looking for. The company also plans to present its software to schools and businesses for presence-oriented applications

Cameras that recognize

The hardware side of the face recognition application specification is the selection of a car video camera. "Monochrome cameras are more responsive to light sensitivity, so they are ideal for low light indoor and outdoor," says Mike Fussell, marketing manager at the FLIR Systems, Inc. (Wilsonville, Oregon) integrated imaging division. "If someone is heavily backlit or shaded, the latest generation of high performance CMOS sensors truly shine in these difficult situations."

FLIR offers customers with better performance in comparison to gentle light conditions with higher levels of sensors, prices and global shutter. The full pixel count can be read at once, eliminating the distortion caused by the rolling shutter on less expensive sensors, says Fussell. Roller chambers show distortions of object movement in relation to shutter speeds, but in low light conditions they offer a cheaper alternative.

Most cameras used in Face Detection are in the range of 3-5 MP, according to Fussell. But in an application such as a passport where all variables are checked, a lower resolution camera is suitable. FLIR also offers stereo visual products calibrated by customers for optical tracking and measuring eye movement relative to the head.

Some companies have led the concept of facial recognition to the next level by walking analysis, studying human motion. "In the building automation application where you want to learn people's habits, you can keep track of how to turn lights on or off, or lifts are foreseen for them," says Fussell.

Overcoming obstacles with the head

Facing all the challenges of face recognition technology, you face the underlying challenges before an algorithm hits the camera or mobile device. According to a study, facial recognition systems are 5 to 10 percent less accurate when trying to identify African Americans as compared to whites. In addition, female subjects are more difficult to detect than men, and younger subjects are more difficult to identify than adults.

Algorithm developers should therefore focus more on the content and quality of training data by distributing data sets evenly across demographic data. Testing the Face Detection System currently offered by the National Institute of Standards and Technology (NIST) can improve accuracy

When the algorithm reaches the camera, the accuracy of face recognition depends on the number and quality of the photos. comparative database. And although most face recognition technologies are automated, most systems require human testing to make the final match. Without professional training, human surveyors make a bad decision about the time that is half the match.

However, the machine vision industry is no stranger to a technology becoming mature. If facial recognition does this, camera manufacturers and software vendors are ready to deliver the devices and services for safe and accurate identity verification.